'Object classification' presentation slideshows

ETISEO

860 views • 74 slides

ETISEO

752 views • 74 slides

Large-Scale Object Recognition using Label Relation Graphs

Large-Scale Object Recognition using Label Relation Graphs. Jia Deng 1,2 , Nan Ding 2 , Yangqing Jia 2 , Andrea Frome 2 , Kevin Murphy 2 , Samy Bengio 2 , Yuan Li 2 , Hartmut Neven 2 , Hartwig Adam 2. University of Michigan 1 , Google 2. Object Classification.

683 views • 37 slides

Global and Efficient Self-Similarity for Object Classification and Detection

Global and Efficient Self-Similarity for Object Classification and Detection. Thomas Deselaers and Vittorio Ferrari. CALVIN group Computer Vision Laboratory ETH Zurich Switzerland. CVPR 2010. Conventional Image Descriptors. Measure direct image properties. gradients. colors.

399 views • 28 slides

3D Object Modelling and Classification

Visual Perception and Robotic Manipulation Springer Tracts in Advanced Robotics. 3D Object Modelling and Classification. Chapter 4. Intelligent Robotics Research Centre (IRRC) Department of Electrical and Computer Systems Engineering Monash University, Australia. Geoffrey Taylor

446 views • 26 slides

Human Motion Analysis

1.37k views • 18 slides

National Virtual Observatory Summer School: Welcome

T HE US N ATIONAL V IRTUAL O BSERVATORY. National Virtual Observatory Summer School: Welcome. Tom McGlynn NASA/GSFC. Outline. Purpose of the NVO and Summer School How to get information Faculty and Staff Students Facilities and logistics Schedule Student exercises and projects

250 views • 17 slides

A Novel GA-SVM Model For Vehicles And Pedestrial Classification In Videos

The paper presents a novel algorithm for object classification in videos based on improved support vector machine (SVM) and genetic algorithm. One of the problems of support vector machine is selection of the appropriate parameters for the kernel. This has affected the accuracy of the SVM over the years. This research aims at optimizing the SVM Radial Basis kernel parameters using the genetic algorithm. Moving object classification is a requirement in smart visual surveillance systems as it allows the system to know the kind of object in the scene and be able to recognize the actions the object can perform. This paper presents an GA-SVM machine learning approach for real time object classification in videos. Radial distance signal features are extracted from the silhouettes of object detected in videos. The radial distance signals features are then normalized and fed into the GA-SVM model. The classification rate of 99.39% is achieved with the genetically trained SVM algorithm while 99.1% classification accuracy is achieved with the normal SVM. A comparison of this classifier with some other classifiers in terms of classification accuracy shows a better performance than other classifiers such as the normal SVM, Artificial neural network (ANN), Genetic Artificial neural network (GANN), K-nearest neighbor (K-NN) and K-Means classifiers. Akintola Kolawole G."A Novel GA-SVM Model For Vehicles And Pedestrial Classification In Videos" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-4 , June 2017, URL: http://www.ijtsrd.com/papers/ijtsrd109.pdf http://www.ijtsrd.com/computer-science/artificial-intelligence/109/a-novel-ga-svm-model-for-vehicles-and-pedestrial-classification-in-videos/akintola-kolawole-g

122 views • 11 slides

Small K. Medium K. Large K. Objective. Training Error (%). Test Error (%). CCCP. SPL. h. x. y. h. x. 1 vs. 7. 2 vs. 7. 3 vs. 8. 8 vs. 9. Iteration 1. Iteration 3. Iteration 5. Iteration 7. Self-Paced Learning for Latent Variable Models.

111 views • 1 slides